From associations to sarcasm: Mining the shift of opinions regarding the Supreme Court on twitter

Sandhu, Mannila; Vinson, C. Danielle; Mago, Vijay; Giabbanelli, Philippe J.

doi:10.1016/j.osnem.2019.100054

Cited by 18 publications

(9 citation statements)

References 36 publications

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“…Twitter has been used for social debates and expressions of public opinion (e.g., [ 33 ]). No doubt, it will continue to be used in this manner for topics of large, public impact.…”

Section: Manual Analysis Of Ad Hoc Tweetsmentioning

confidence: 99%

Text Analysis of Evolving Emotions and Sentiments in COVID-19 Twitter Communication

O’Leary

2022

Cogn Comput

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Scientists and regular citizens alike search for ways to manage the widespread effects of the COVID-19 pandemic. While scientists are busy in their labs, other citizens often turn to online sources to report their experiences and concerns and to seek and share knowledge of the virus. The text generated by those users in online social media platforms can provide valuable insights about evolving users’ opinions and attitudes. The objective of this research is to analyze text of such user disclosures to study human communication during a pandemic in four primary ways. First, we analyze Twitter tweet information, generated throughout the pandemic, to understand users’ communications concerning COVID-19 and how those communications have evolved during the pandemic. Second, we analyze linguistic sentiment concepts (analytic, authentic, clout, and tone concepts) in different Twitter settings (sentiment in tweets with pictures or no pictures and tweets versus retweets). Third, we investigate the relationship between Twitter tweets with additional forms of internet activity, namely, Google searches and Wikipedia page views. Finally, we create and use a dictionary of specific COVID-19-related concepts (e.g., symptom of lost taste) to assess how the use of those concepts in tweets are related to the spread of information and the resulting influence of Twitter users. The analysis showed a surprisingly lack of emotion in the initial phases of the pandemic as people were information seeking. As time progressed, there were more expressions of sentiment, including anger. Further, tweets with and without pictures and/or video had statistically significant differences in text sentiment characteristics. Similarly, there were differences between the sentiment in tweets and retweets and tweets. We also found that Google and Wikipedia searches were predictive of sentiment in the tweets. Finally, a variable representing a dictionary of COVID-related concepts was statistically significant when related to users’ Twitter influence score and number of retweets, illustrating the general impact of COVID-19 on Twitter and human communication. Overall, the results provide insights into human communication as well as models of human internet and social media use. These findings could be useful for the management of global challenges beyond, or different from, a pandemic.

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“…Twitter has been used for social debates and expressions of public opinion (e.g., [ 33 ]). No doubt, it will continue to be used in this manner for topics of large, public impact.…”

Section: Manual Analysis Of Ad Hoc Tweetsmentioning

confidence: 99%

Text Analysis of Evolving Emotions and Sentiments in COVID-19 Twitter Communication

O’Leary

2022

Cogn Comput

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“…In Twitter research, pre-processing often leads to removing most of the data. For example, our previous research on Twitter regarding the Supreme Court ( Sandhu et al, 2019 ) discarded 87−89% of the data, while our examination of Twitter and obesity discarded 73% of the data ( Sandhu, Giabbanelli & Mago, 2019 ). The reason is that pre-processing has historically involved a series of filters ( e.g ., removing words that are not deemed informative in English, removing hashtags and emojis), which were necessary as the analysis model could not satisfactorily cope with raw data.…”

Section: Resultsmentioning

confidence: 96%

(Re)shaping online narratives: when bots promote the message of President Trump during his first impeachment

Galgoczy

Phatak

Vinson

et al. 2022

PeerJ Computer Science

Self Cite

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Influencing and framing debates on Twitter provides power to shape public opinion. Bots have become essential tools of ‘computational propaganda’ on social media such as Twitter, often contributing to a large fraction of the tweets regarding political events such as elections. Although analyses have been conducted regarding the first impeachment of former president Donald Trump, they have been focused on either a manual examination of relatively few tweets to emphasize rhetoric, or the use of Natural Language Processing (NLP) of a much larger corpus with respect to common metrics such as sentiment. In this paper, we complement existing analyses by examining the role of bots in the first impeachment with respect to three questions as follows. (Q1) Are bots actively involved in the debate? (Q2) Do bots target one political affiliation more than another? (Q3) Which sources are used by bots to support their arguments? Our methods start with collecting over 13M tweets on six key dates, from October 6th 2019 to January 21st 2020. We used machine learning to evaluate the sentiment of the tweets (via BERT) and whether it originates from a bot. We then examined these sentiments with respect to a balanced sample of Democrats and Republicans directly relevant to the impeachment, such as House Speaker Nancy Pelosi, senator Mitch McConnell, and (then former Vice President) Joe Biden. The content of posts from bots was further analyzed with respect to the sources used (with bias ratings from AllSides and Ad Fontes) and themes. Our first finding is that bots have played a significant role in contributing to the overall negative tone of the debate (Q1). Bots were targeting Democrats more than Republicans (Q2), as evidenced both by a difference in ratio (bots had more negative-to-positive tweets on Democrats than Republicans) and in composition (use of derogatory nicknames). Finally, the sources provided by bots were almost twice as likely to be from the right than the left, with a noticeable use of hyper-partisan right and most extreme right sources (Q3). Bots were thus purposely used to promote a misleading version of events. Overall, this suggests an intentional use of bots as part of a strategy, thus providing further confirmation that computational propaganda is involved in defining political events in the United States. As any empirical analysis, our work has several limitations. For example, Trump’s rhetoric on Twitter has previously been characterized by an overly negative tone, thus tweets detected as negative may be echoing his message rather than acting against him. Previous works show that this possibility is limited, and its existence would only strengthen our conclusions. As our analysis is based on NLP, we focus on processing a large volume of tweets rather than manually reading all of them, thus future studies may complement our approach by using qualitative methods to assess the specific arguments used by bots.

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“…The more text that associates fast fashion brands with pollution, the higher the confidence value would be. The context can also help to identify more relevant concepts, which can be used for further questions [77]. For instance, 'sustainable development' is mentioned as part of the answer 'global climate change', and it could lead to another line of questioning by asking the Q&A system, "What types of sustainable development are happening in the fashion industry?".…”

Section: Natural Language Processingmentioning

confidence: 99%

Automatically Generating Scenarios from a Text Corpus: A Case Study on Electric Vehicles

2022

Self Cite

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Creating ‘what-if’ scenarios to estimate possible futures is a key component of decision-making processes. However, this activity is labor intensive as it is primarily done manually by subject-matter experts who start by identifying relevant themes and their interconnections to build models, and then craft diverse and meaningful stories as scenarios to run on these models. Previous works have shown that text mining could automate the model-building aspect, for example, by using topic modeling to extract themes from a large corpus and employing variations of association rule mining to connect them in quantitative ways. In this paper, we propose to further automate the process of scenario generation by guiding pre-trained deep neural networks (i.e., BERT) through simulated conversations to extract a model from a corpus. Our case study on electric vehicles shows that our approach yields similar results to previous work while almost eliminating the need for manual involvement in model building, thus focusing human expertise on the final stage of crafting compelling scenarios. Specifically, by using the same corpus as a previous study on electric vehicles, we show that the model created here either performs similarly to the previous study when there is a consensus in the literature, or differs by highlighting important gaps on domains such as government deregulation.

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From associations to sarcasm: Mining the shift of opinions regarding the Supreme Court on twitter

Cited by 18 publications

References 36 publications

Text Analysis of Evolving Emotions and Sentiments in COVID-19 Twitter Communication

Text Analysis of Evolving Emotions and Sentiments in COVID-19 Twitter Communication

(Re)shaping online narratives: when bots promote the message of President Trump during his first impeachment

Automatically Generating Scenarios from a Text Corpus: A Case Study on Electric Vehicles

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